[{"data":1,"prerenderedAt":903},["ShallowReactive",2],{"insight-\u002Fget-inspired\u002Finsights\u002F50-tokens-sec-on-a-desktop-running-llms-on-the-nvidia-dgx-spark":3,"insights-article-4":626,"relation-people":663,"insights-all-6":875},{"item":4,"authors":620},{"id":5,"title":6,"applePodcastsUrl":7,"audioLink":7,"authors":8,"body":10,"coverImage":605,"description":606,"draft":607,"duration":7,"episodeNumber":7,"extension":608,"format":609,"host":7,"issue":7,"meta":610,"mockups":611,"navigation":612,"path":613,"pdf":7,"publishedAt":614,"readLink":7,"sectors":615,"seo":616,"spotifyUrl":7,"stem":617,"tags":618,"transcriptPdf":7,"upcoming":607,"videoLink":7,"year":7,"youtubeUrl":7,"__hash__":619},"insights\u002Fget-inspired\u002Finsights\u002F50-tokens-sec-on-a-desktop-running-llms-on-the-nvidia-dgx-spark.md","50+ Tokens\u002Fsec on a Desktop: Running LLMs on the NVIDIA DGX Spark",null,[9],"luc-bocahut",{"type":11,"value":12,"toc":590},"minimark",[13,41,46,54,57,79,86,90,93,125,132,139,143,149,160,165,172,175,187,190,194,201,207,210,215,223,226,230,235,241,245,252,255,277,284,290,294,297,303,334,340,344,348,353,356,361,368,372,375,378,381,384,387,390,393,396,399,402,405,408,411,414,417,428,434,438,448,454,463,469,478,484,486,489,493,504,514,517,519,522,529,533,544,553,568,577,586],[14,15,16,20,21,24,25,28,29,32,33,36,37,40],"p",{},[17,18,19],"strong",{},"TL;DR:"," At ",[17,22,23],{},"XRPL Commons",", we’ve been experimenting with ",[17,26,27],{},"local AI infrastructure"," for development workflows: coding assistants, document drafting, agent systems, and internal tooling. We got a ",[17,30,31],{},"30B-parameter LLM running at 51–54 tokens\u002Fsec"," on the ",[17,34,35],{},"NVIDIA DGX Spark"," by combining ",[17,38,39],{},"Mixture-of-Experts models, FP8 quantization, and a community Docker image that fixes Blackwell compatibility issues."," Full setup below, brought to you by our CTO.",[42,43,45],"h2",{"id":44},"why-we-wanted-local-llms","Why We Wanted Local LLMs",[14,47,48,49,24,51,53],{},"At ",[17,50,23],{},[17,52,27],{}," for development workflows: coding assistants, document drafting, agent systems, and internal tooling.",[14,55,56],{},"Our requirements were simple:",[58,59,60,67,73],"ul",{},[61,62,63,66],"li",{},[17,64,65],{},"Fast enough"," for interactive use",[61,68,69,72],{},[17,70,71],{},"Private"," enough to run on-premise",[61,74,75,78],{},[17,76,77],{},"Replicable"," across multiple machines",[14,80,81,82,85],{},"The ",[17,83,84],{},"DGX Spark"," looked like an interesting candidate. But achieving good performance requires understanding its real constraint.",[42,87,89],{"id":88},"the-hardware","The Hardware",[14,91,92],{},"The DGX Spark packs significant compute into a desktop system:",[58,94,95,100,105,110,115,120],{},[61,96,97],{},[17,98,99],{},"NVIDIA GB10 Blackwell GPU (SM 12.1)",[61,101,102],{},[17,103,104],{},"128 GB unified LPDDR5X memory",[61,106,107],{},[17,108,109],{},"273 GB\u002Fs memory bandwidth",[61,111,112],{},[17,113,114],{},"ARM Grace CPU (20 cores: 10 Cortex-X925 + 10 Cortex-A725)",[61,116,117],{},[17,118,119],{},"4 TB NVMe M.2 (~3.7 TB usable)",[61,121,122],{},[17,123,124],{},"DGX OS (Ubuntu 24.04)",[14,126,127,128,131],{},"The standout feature is the ",[17,129,130],{},"128 GB unified memory",", which allows very large models to run locally.",[14,133,134,135,138],{},"But the critical limitation is ",[17,136,137],{},"memory bandwidth",".",[42,140,142],{"id":141},"the-bandwidth-wall","The Bandwidth Wall",[14,144,145,146,138],{},"LLM inference is primarily ",[17,147,148],{},"memory-bandwidth bound",[14,150,151,152,155,156,159],{},"During autoregressive decoding, each token requires reading the ",[17,153,154],{},"active model weights"," from memory. With ",[17,157,158],{},"273 GB\u002Fs bandwidth",", the limits become clear:",[14,161,162],{},[17,163,164],{},"Model",[14,166,167],{},[168,169],"img",{"alt":170,"src":171},"","\u002Fimages\u002Fblog\u002F50-tokens-sec-on-a-desktop-running-llms-on-the-nvidia-dgx-spark\u002F1.webp",[14,173,174],{},"Our first runs matched this almost exactly:",[58,176,177,182],{},[61,178,179],{},[17,180,181],{},"Qwen3-32B (bf16): 3.7 tok\u002Fs",[61,183,184],{},[17,185,186],{},"Qwen3-8B (bf16): 13.1 tok\u002Fs",[14,188,189],{},"Large models fit comfortably in memory, but generate tokens slowly.",[42,191,193],{"id":192},"the-moe-breakthrough","The MoE Breakthrough",[14,195,196,197,200],{},"The solution is ",[17,198,199],{},"Mixture-of-Experts (MoE)"," models.",[14,202,203,204,138],{},"Instead of activating the entire network, MoE models route each token through a ",[17,205,206],{},"subset of experts",[14,208,209],{},"Example:",[14,211,212],{},[17,213,214],{},"Qwen3-30B-A3B",[58,216,217,220],{},[61,218,219],{},"~30.5B total parameters",[61,221,222],{},"~3.3B active parameters per token",[14,224,225],{},"This dramatically changes the bandwidth math:",[14,227,228],{},[17,229,164],{},[14,231,232],{},[168,233],{"alt":170,"src":234},"\u002Fimages\u002Fblog\u002F50-tokens-sec-on-a-desktop-running-llms-on-the-nvidia-dgx-spark\u002F2.webp",[14,236,237,238,138],{},"In practice, routing overhead, KV-cache reads, and software stack inefficiencies reduce throughput. Real systems typically achieve ",[17,239,240],{},"50–70% of theoretical bandwidth limits",[42,242,244],{"id":243},"the-blackwell-software-problem","The Blackwell Software Problem",[14,246,247,248,251],{},"The DGX Spark’s ",[17,249,250],{},"Blackwell GPU (SM 12.1)"," is new enough that much of the software stack is still catching up.",[14,253,254],{},"Issues we encountered:",[58,256,257,262,267,272],{},[61,258,259],{},[17,260,261],{},"FlashAttention 2 crashes",[61,263,264],{},[17,265,266],{},"vLLM MoE kernels missing SM 12.1",[61,268,269],{},[17,270,271],{},"PyTorch officially supports only SM 12.0",[61,273,274],{},[17,275,276],{},"CUDA graphs disabled in standard builds",[14,278,279,280,283],{},"We initially built ",[17,281,282],{},"vLLM from source",", patching build scripts and dependencies.",[14,285,286,287,138],{},"It worked, but required --enforce-eager mode (no CUDA graphs), which capped throughput at about ",[17,288,289],{},"30 tok\u002Fs",[42,291,293],{"id":292},"the-avarok-docker-image","The Avarok Docker Image",[14,295,296],{},"A community project solved most of these issues.",[14,298,81,299,302],{},[17,300,301],{},"Avarok dgx-vllm Docker image"," includes:",[58,304,305,311,316,322,328],{},[61,306,307,308],{},"Patched ",[17,309,310],{},"vLLM v0.16.0rc2",[61,312,313],{},[17,314,315],{},"SM 12.1 Blackwell support",[61,317,318,319],{},"Custom ",[17,320,321],{},"CUTLASS kernels",[61,323,324,325],{},"Software fallback for missing ",[17,326,327],{},"NVFP4 instructions",[61,329,330,331],{},"Working ",[17,332,333],{},"FlashAttention and CUDA graphs",[14,335,336,337,138],{},"Instead of hours compiling from source, deployment becomes a ",[17,338,339],{},"single Docker command",[42,341,343],{"id":342},"results","Results",[14,345,346],{},[17,347,164],{},[14,349,350],{},[168,351],{"alt":170,"src":352},"\u002Fimages\u002Fblog\u002F50-tokens-sec-on-a-desktop-running-llms-on-the-nvidia-dgx-spark\u002F3.webp",[14,354,355],{},"The winning combination:",[14,357,358],{},[17,359,360],{},"MoE architecture + FP8 quantization + CUDA graphs via Avarok Docker",[14,362,363,364,367],{},"The FP8 model uses about ",[17,365,366],{},"110 GB of GPU memory",", leaving little headroom but delivering excellent throughput.",[42,369,371],{"id":370},"final-setup","Final Setup",[14,373,374],{},"Deployment is straightforward:",[14,376,377],{},"shell",[14,379,380],{},"docker pull avarok\u002Fdgx-vllm-nvfp4-kernel:v22",[14,382,383],{},"docker run -d \\",[14,385,386],{},"--name vllm \\",[14,388,389],{},"--gpus all \\",[14,391,392],{},"--shm-size=16g \\",[14,394,395],{},"--restart unless-stopped \\",[14,397,398],{},"-p 8000:8888 \\",[14,400,401],{},"-v \u002Fhome\u002F$USER\u002F.cache\u002Fhuggingface:\u002Froot\u002F.cache\u002Fhuggingface \\",[14,403,404],{},"-e MODEL=Qwen\u002FQwen3-30B-A3B-Instruct-2507-FP8 \\",[14,406,407],{},"-e PORT=8888 \\",[14,409,410],{},"-e GPU_MEMORY_UTIL=0.85 \\",[14,412,413],{},"-e MAX_MODEL_LEN=32768 \\",[14,415,416],{},"avarok\u002Fdgx-vllm-nvfp4-kernel:v22 serve",[14,418,419,420,423,424,427],{},"First startup takes ",[17,421,422],{},"10–20 minutes"," (model download and CUDA graph capture). After that, the container auto-starts and exposes an ",[17,425,426],{},"OpenAI-compatible API",":",[14,429,430],{},[431,432,433],"code",{},"http:\u002F\u002Flocalhost:8000\u002Fv1",[42,435,437],{"id":436},"lessons-learned","Lessons Learned",[14,439,440,441,444,445,138],{},"**1. Understand the bottleneck ",[442,443],"br",{},"\n**On DGX Spark, ",[17,446,447],{},"memory bandwidth determines performance",[14,449,450,451,453],{},"**2. MoE models are ideal for bandwidth-limited systems ",[442,452],{},"\n**They dramatically reduce active weights per token.",[14,455,456,457,459,460,138],{},"**3. FP8 quantization is a free win ",[442,458],{},"\n**Throughput nearly doubled from ",[17,461,462],{},"30 → 51 tok\u002Fs",[14,464,465,466,468],{},"**4. Avoid building from source if possible ",[442,467],{},"\n**Community builds often include critical patches ahead of official releases.",[14,470,471,472,474,475,138],{},"**5. Stop competing inference servers first ",[442,473],{},"\n**One Spark OOM-killed during installation because ",[17,476,477],{},"Ollama was using ~100 GB of memory",[14,479,480,481,483],{},"**6. Kernel updates can break NVIDIA drivers ",[442,482],{},"\n**If nvidia-smi fails after reboot:",[14,485,377],{},[14,487,488],{},"sudo apt install linux-modules-nvidia-580-open-$(uname -r)",[42,490,492],{"id":491},"next-experiments","Next Experiments",[14,494,495,496,499,500,503],{},"One promising direction is experimenting with ",[17,497,498],{},"reasoning-optimized models",". A recent example is ",[17,501,502],{},"Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled"," (“Qwopus”), which distills structured reasoning from Claude 4.6 Opus into a Qwen3.5 base model.",[14,505,506,507,510,511,138],{},"While it is a ",[17,508,509],{},"dense model"," and therefore likely slower than our current MoE setup on the DGX Spark, it may offer ",[17,512,513],{},"stronger step-by-step reasoning for coding, math, and agent workflows",[14,515,516],{},"Testing it is simple—swap the model in the same container:",[14,518,377],{},[14,520,521],{},"-e MODEL=Jackrong\u002FQwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled",[14,523,524,525,528],{},"We plan to benchmark ",[17,526,527],{},"reasoning quality vs throughput"," alongside the current MoE setup.",[42,530,532],{"id":531},"references","References",[14,534,535,536,538],{},"NVIDIA DGX Spark ",[442,537],{},[539,540,541],"a",{"href":541,"rel":542},"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Fdgx-spark\u002F",[543],"nofollow",[14,545,546,547,549],{},"Avarok dgx-vllm Docker project ",[442,548],{},[539,550,551],{"href":551,"rel":552},"https:\u002F\u002Fgithub.com\u002Favarok-ai\u002Fdgx-vllm",[543],[14,554,555,556,558,562,563,567],{},"vLLM documentation ",[442,557],{},[539,559,560],{"href":560,"rel":561},"https:\u002F\u002Fdocs.vllm.ai",[543],"(",[539,564,565],{"href":565,"rel":566},"https:\u002F\u002Fdocs.vllm.ai\u002F",[543],")",[14,569,570,571,573],{},"Qwen3-30B-A3B FP8 model ",[442,572],{},[539,574,575],{"href":575,"rel":576},"https:\u002F\u002Fhuggingface.co\u002FQwen\u002FQwen3-30B-A3B-Instruct-2507-FP8",[543],[14,578,579,580,582],{},"Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled ",[442,581],{},[539,583,584],{"href":584,"rel":585},"https:\u002F\u002Fhuggingface.co\u002FJackrong\u002FQwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled",[543],[42,587,589],{"id":588},"let-us-know-what-youre-building","Let us know what you’re building.",{"title":170,"searchDepth":591,"depth":591,"links":592},2,[593,594,595,596,597,598,599,600,601,602,603,604],{"id":44,"depth":591,"text":45},{"id":88,"depth":591,"text":89},{"id":141,"depth":591,"text":142},{"id":192,"depth":591,"text":193},{"id":243,"depth":591,"text":244},{"id":292,"depth":591,"text":293},{"id":342,"depth":591,"text":343},{"id":370,"depth":591,"text":371},{"id":436,"depth":591,"text":437},{"id":491,"depth":591,"text":492},{"id":531,"depth":591,"text":532},{"id":588,"depth":591,"text":589},"\u002Fimages\u002Fblog\u002F50-tokens-sec-on-a-desktop-running-llms-on-the-nvidia-dgx-spark\u002Fcover.webp","Local AI doesn’t have to mean slow. We got a 30B LLM running at 51–54 tok\u002Fs on a DGX Spark using MoE models, FP8 quantization, and a patched Blackwell stack. See how we made fast, private on‑prem AI actually work.",false,"md","article",{},[],true,"\u002Fget-inspired\u002Finsights\u002F50-tokens-sec-on-a-desktop-running-llms-on-the-nvidia-dgx-spark","2026-03-19",[],{"title":6,"description":606},"get-inspired\u002Finsights\u002F50-tokens-sec-on-a-desktop-running-llms-on-the-nvidia-dgx-spark",[],"_9rrMhAMaAdvkmPo-eWw-PMqzp4-hYYdJgv4Sc3Jfec",[621],{"stem":622,"name":623,"title":624,"photo":625},"people\u002Fluc-bocahut","Luc Bocahut","Product Director & Solutions Architect","\u002Fimages\u002Fpeople\u002Fluc-bocahut.webp",{"formats":627,"items":631},[628,609,629,630],"annual-report","magazine","podcast",[632,640,648,656],{"path":633,"title":634,"description":635,"publishedAt":636,"authors":637,"format":609,"issue":7,"year":7,"episodeNumber":7,"upcoming":607,"coverImage":639},"\u002Fget-inspired\u002Finsights\u002Fscaling-the-xrp-ledger-in-poland-insights-from-our-carpathian-startup-fest-business-breakfast","Scaling the XRP Ledger in Poland: Insights from Our Carpathian Startup Fest Business Breakfast","Blockchain adoption doesn't happen at the protocol level — it happens when global infrastructure meets local ecosystems. Here's how XRPL Commons and Neti brought that conversation to one of Europe's fastest-growing innovation hubs.","2026-06-26",[638],"neti-poland","\u002Fimages\u002Fblog\u002Fscaling-the-xrp-ledger-in-poland-insights-from-our-carpathian-startup-fest-business-breakfast\u002Fcover.webp",{"path":641,"title":642,"description":643,"publishedAt":644,"authors":645,"format":609,"issue":7,"year":7,"episodeNumber":7,"upcoming":607,"coverImage":647},"\u002Fget-inspired\u002Finsights\u002Fwhere-research-meets-the-real-world-recapping-the-xrpl-blockchain-research-summit-2026","Where Research Meets the Real World. Recapping the XRPL Blockchain Research Summit 2026","The Blockchain Research Summit 2026 confirmed that academic blockchain research has moved beyond theory, with live demos, ZK-proof architectures, and deployable systems now sitting alongside policy and curriculum debates. Here is what two days of working groups, PhD showcases, and cross-sector dialogue revealed about where decentralised infrastructure is actually heading.","2026-06-23",[646],"vera-radeva-hadjiev","\u002Fimages\u002Fblog\u002Fwhere-research-meets-the-real-world-recapping-the-xrpl-blockchain-research-summit-2026\u002Fcover.webp",{"path":649,"title":650,"description":651,"publishedAt":652,"authors":653,"format":609,"issue":7,"year":7,"episodeNumber":7,"upcoming":607,"coverImage":655},"\u002Fget-inspired\u002Finsights\u002Ffrom-nfc-to-xrpl-our-first-meetup-in-lisbon","From NFC to XRPL: Our First Meetup in Lisbon","What happens when blockchain meets Lisbon's buzzing startup scene? XRPL Commons brought together 70 founders, developers, and builders for their very first meetup in Portugal — and it was only the beginning.","2026-06-20",[654],"zsofi-borsi","\u002Fimages\u002Fblog\u002Ffrom-nfc-to-xrpl-our-first-meetup-in-lisbon\u002Fcover.webp",{"path":657,"title":658,"description":659,"publishedAt":660,"authors":661,"format":609,"issue":7,"year":7,"episodeNumber":7,"upcoming":607,"coverImage":662},"\u002Fget-inspired\u002Finsights\u002Fglow-whats-changing-and-why","GLOW : What’s Changing and Why","GLOW is a retroactive funding program for builders in the XRPL ecosystem. Six months after launch, we took an honest look at what worked and what didn't. The program is now open to direct applications, no referral needed. Here's what we learned and where we go from here.","2026-06-16",[9],"\u002Fimages\u002Fblog\u002Fglow-whats-changing-and-why\u002Fcover.webp",[664,667,670,673,676,679,682,685,688,691,694,697,700,703,706,709,712,715,718,721,724,727,730,733,736,739,742,745,748,751,754,757,760,763,766,769,772,773,776,779,782,785,788,791,794,797,800,803,806,809,812,815,818,821,824,827,830,833,836,839,842,845,848,851,854,857,860,863,866,869,872],{"stem":665,"name":666},"people\u002Fadria-carrera-mas","Adrià Carrera Mas",{"stem":668,"name":669},"people\u002Falex-mavigok","Alex Mavigok",{"stem":671,"name":672},"people\u002Falexandre-duarte","Alexandre Duarte",{"stem":674,"name":675},"people\u002Famaury-mongreville","Amaury Mongreville",{"stem":677,"name":678},"people\u002Fartur-kirjakulov-phd","Artur Kirjakulov, PhD",{"stem":680,"name":681},"people\u002Fasheesh-birla","Asheesh Birla",{"stem":683,"name":684},"people\u002Faurelien-burget","Aurélien 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Moha",{"stem":728,"name":729},"people\u002Felisa-bailly","Elisa Bailly",{"stem":731,"name":732},"people\u002Ffabiola-bernard","Fabiola Bernard",{"stem":734,"name":735},"people\u002Ffanny-brakchi","Fanny Brakchi",{"stem":737,"name":738},"people\u002Fferran-prat-tio","Ferran Prat Tió",{"stem":740,"name":741},"people\u002Ffig-squid-router","Fig",{"stem":743,"name":744},"people\u002Fflorence-tison","Florence Tison",{"stem":746,"name":747},"people\u002Fflorent-uzio","Florent Uzio",{"stem":749,"name":750},"people\u002Fflorian-alonso","Florian Alonso",{"stem":752,"name":753},"people\u002Ffrederic-martin","Frédéric Martin",{"stem":755,"name":756},"people\u002Fguillaume-evrat","Guillaume Evrat",{"stem":758,"name":759},"people\u002Fguy-levi-bochi","Guy Lévi-Bochi",{"stem":761,"name":762},"people\u002Fimad-el-aouny","Imad EL Aouny",{"stem":764,"name":765},"people\u002Fkrippenreiter","Krippenreiter",{"stem":767,"name":768},"people\u002Flara-pagnier","Lara 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